mlsanityData to 14 Oct 2026

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data

arXiv 2009.02763 ↗First posted 6 Sep 2020cs.LG cs.AI stat.MLcited by 22

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Abstract (arXiv)

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w.r.t. training time, accuracy, etc., comparing to idealized non-private VFL. Our work builds on the recent advances in VFL-based collaborative training among different organizations which rely on protocols like Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC) to secure computation and training. In particular, we analyze how VFL's intermediate result (IR) can leak private information of the training data during communication and design a DP-based privacy-preserving algorithm to ensure the data confidentiality of VFL participants. We mathematically prove that our algorithm not only provides utility guarantees for VFL, but also offers multi-level privacy, i.e. DP w.r.t. IR and joint differential privacy (JDP) w.r.t. model weights. Experimental results demonstrate that our work, under adequate privacy budgets, is quantitatively and qualitatively similar to GLMs, learned in idealized non-private VFL setting, rather than the increased cost in memory and processing time in most prior works based on HE or MPC. Our codes will be released if this paper is accepted.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2020.

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Citing papers per year: 2021: 1; 2022: 8; 2023: 5; 2024: 6; 2025: 1; 2026: 1.

22 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  3. 2022A Survey on Heterogeneous Federated Learningcited by 42
  4. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  5. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  6. 2022Differentially Private Label Protection in Split Learningcited by 26
  7. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  8. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  9. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  10. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  11. 2022Differentially Private Vertical Federated Clusteringcited by 10
  12. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
10 more
  1. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  2. 2022Subject Granular Differential Privacy in Federated Learningcited by 4
  3. 2022Differentially Private Vertical Federated Learningcited by 4
  4. 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningcited by 3
  5. 2025The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communicationscited by 3
  6. 2023FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregationcited by 2
  7. 2023Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddingscited by 1
  8. 2024A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoTcited by 1
  9. 2023Differentially Private Distributed Convex Optimizationcited by 0
  10. 2026Secure and Privacy-Preserving Vertical Federated Learningcited by 0